Systems and methods for providing personalized audio replay on a plurality of consumer devices

ABSTRACT

Systems and methods for processing an audio signal are provided for server-mediated sound personalization on a plurality of consumer devices. A user hearing test is conducted on one of a plurality of audio output devices. Next, the hearing data of the user&#39;s hearing test is outputted to a server and stored on the server&#39;s database along with a unique user identifier. Next, a set of DSP parameters for a sound personalization algorithm are calculated from the user&#39;s hearing data. The DSP parameter set is then outputted to one of a plurality of audio output devices when the user logs in with their unique identifier on an application on the audio output device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/868,775, filed May 7, 2020, which is a continuation of U.S.application Ser. No. 16/540,345, filed Aug. 14, 2019, which are hereinincorporated by reference in its entirety.

FIELD OF INVENTION

This invention relates generally to the field of digital signalprocessing (DSP), audio engineering and audiology—more specificallysystems and methods for providing server-mediated sound personalizationon a plurality of consumer devices based on user hearing test results.

BACKGROUND

Traditional DSP sound personalization methods often rely onadministration of an audiogram to parameterize a frequency gaincompensation function. Typically, a pure tone threshold (PTT) hearingtest is employed to identify frequencies in which a user exhibits raisedhearing thresholds and the frequency output is modulated accordingly.These gain parameters are stored locally on the user's device forsubsequent audio processing.

However, this approach to augmenting the sound experience for the useris imprecise and inefficient. As hearing test results are stored locallyon a single device, the resulting parameter calculations areinaccessible to a central server, as well as other devices. To thisextent, separate hearing tests must be conducted on everydevice—potentially leading to locally incorrect results and inconsistentparameter values stored on different audio output devices. The abilityto take hearing tests on multiple devices linked to a core account: 1)encourages users to take tests on whatever device pairing is mostconvenient at the time, 2) improves accuracy through the consolidationof multiple test results, and 3) enables the tracking of a user'shearing state over time. Additionally, in the instance of aberranthearing test results, the user can be informed if he or she is using animproperly calibrated device and/or if the hearing test was conductedimproperly.

The use of frequency compensation is further inadequate to the extentthat solely applying a gain function to the audio signal does notsufficiently restore audibility. The gain may enable the user torecapture previously unheard frequencies, but the user may subsequentlyexperience loudness discomfort. Listeners with sensorineural hearingloss typically have similar, or even reduced, discomfort thresholds whencompared to normal hearing listeners, despite their hearing thresholdsbeing raised. To this extent, their dynamic aperture is narrower andsimply adding gain would be detrimental to their hearing health in thelong run.

Although hearing loss typically begins at higher frequencies, listenerswho are aware that they have hearing loss do not typically complainabout the absence of high frequency sounds. Instead, they reportdifficulties listening in a noisy environment and in hearing out thedetails in a complex mixture of sounds, such as in an audio stream of aradio interview conducted in a busy street. In essence, off frequencysounds more readily mask information with energy in other frequenciesfor hearing-impaired (HI) individuals—music that was once clear and richin detail becomes muddled. This is because music itself is highlyself-masking, i.e. numerous sound sources have energy that overlaps inthe frequency space, which can reduce outright detectability, or impedethe users' ability to extract information from some of the sources.

As hearing deteriorates, the signal-conditioning capabilities of the earbegin to break down, and thus HI listeners need to expend more mentaleffort to make sense of sounds of interest in complex acoustic scenes(or miss the information entirely). A raised threshold in an audiogramis not merely a reduction in aural sensitivity, but a result of themalfunction of some deeper processes within the auditory system thathave implications beyond the detection of faint sounds. To this extent,the addition of simple frequency gain provides an inadequate solution

Accordingly, it is an aspect of the present disclosure to providesystems and methods for providing personalized audio replay on aplurality of consumer devices through a server-empowered soundpersonalization account. By providing more accurate and portableparameter sets, a user may be able to enjoy sound personalization, andconsequently, a healthier listening experience, across a universe ofdevices with one simple hearing test.

SUMMARY OF THE INVENTION

According to an aspect of the present disclosure, provided are systemsand methods for providing personalized audio replay on a plurality ofconsumer devices. In some embodiments, the system and method includesteps comprising: conducting a user hearing test on one of a pluralityof audio output devices, outputting the hearing data from the user'shearing test to a server; storing the hearing data on the server'sdatabase with a user unique identifier; calculating a set of parametersfrom the user's hearing data for a sound personalization algorithm andstoring the parameters alongside the unique user identifier on thedatabase; outputting the set of parameters to the sound personalizationalgorithm on one of a plurality of audio output devices, wherein theparameters are outputted when the user inputs their unique identifier onone of the audio output devices; and processing an audio signal on oneof the audio output devices using the sound personalization algorithm.

In another embodiment, the system and method include steps comprising:conducting a user hearing test on one of a plurality of audio outputdevices; calculating a set of parameters from the user's hearing datafor a sound personalization algorithm on the audio output device;outputting the hearing data and calculated parameters to a server;storing the hearing data and calculated parameters on the server'sdatabase with a unique identifier; outputting the set of parameters tothe sound personalization algorithm on one of a plurality of audiooutput devices, wherein the parameters are outputted when the userinputs their unique identifier to an application running on one of theaudio output devices; and processing an audio signal on one of the audiooutput devices using the sound personalization algorithm.

In another embodiment, the system and method include steps comprising:conducting a user hearing test on one of a plurality of audio outputdevices; outputting the hearing data of the user's hearing test to aserver; storing the hearing data on the server's database with a uniqueidentifier; outputting the hearing data to one of a plurality of audiooutput devices, wherein the hearing data is outputted when the userinputs their unique identifier to an application running on one of theaudio output devices; calculating a set of parameters from the user'shearing data for a sound personalization algorithm on the audio outputdevice; and outputting the parameters to the sound personalizationalgorithm on the audio output device.

In some embodiments, the hearing test is one or more of a thresholdtest, a suprathreshold test, a psychophysical tuning curve test, amasked threshold test, a temporal fine structure test, a speech in noisetest and a temporal masking curve test.

In some embodiments, the parameters are recalculated when the userconducts an additional hearing test on anyone of a plurality of audiooutput devices. The additional hearing test may reflect information fromanother critical band. The additional hearing test may be a differenttype of hearing test from previously conducted hearing tests or it maybe a more recent version of a previously conducted hearing test.Additionally, the additional hearing test may replace the hearing datafrom a previously conducted hearing test with aberrant results.

In some embodiments, the set of parameters is calculated on demand onthe server when the user inputs their unique identifier on one of theaudio output devices.

In some embodiments, the hearing test measures masking threshold curveswithin a range of frequencies from 250 Hz to 12 kHz.

In some embodiments, the sound personalization algorithm operates onsubband signals of the audio signal, i.e. the algorithm works frequencyselectively. In a further embodiment, the parameters of the soundpersonalization algorithm comprise at least one of a gain value providedin each subband and a limiter value provided in each subband. In anoptional embodiment, the sound personalization algorithm may be amultiband dynamic processing algorithm. The parameters of the multibanddynamics processor may optionally comprise at least one of a thresholdvalue of a dynamic range compressor provided in each subband, a ratiovalue of a dynamic range compressor provided in each subband, and a gainvalue provided in each subband. In an alternate embodiment, theparameters of the multiband dynamics processor may optionally bedetermined from a hearing aid gain table, which specify the amount ofgain for a given input level at each frequency. Typically these tablescomprise columns for frequencies and rows for varying intensities ofsound inputs (which also may be mapped to threshold, ratio and gainsettings overall).

In some embodiments, the parameters are calculated indirectly. Forinstance, parameters may be calculated using a best fit of the userhearing data with previously inputted entries within the server'sdatabase, wherein the parameters associated with the best fitting,previously inputted hearing data are copied and inputted into the user'sserver database entry. Best fit may be determined, for instance, bymeasuring average Euclidean distance between the user's hearing data andhearing data in the database. Alternatively, root mean square distanceor a similar best fit measurement may be used.

In some embodiments, the parameters may be calculated using the nearestfit of the user hearing data with at least two hearing data entrieswithin the server's database, wherein the parameters associated with thenearest fitting, previously inputted hearing data are interpolated andinputted into the user's server database entry. For instance, parametersmay be interpolated linearly between two parameter values. Alternately,parameters may be interpolated non-linearly, such as through a squaredfunction. Alternately, the parameters may be calculated from a fittedmathematical function, such as a polynomial function, derived fromplotting existing hearing and parameter set data entries within theserver database.

In some embodiments, the parameters may be calculated by converting theuser's hearing test results into a ‘hearing age’ value. Based uponpredictable declines in hearing function, the user's hearing testresults may be matched to the nearest representative hearing age. Fromthis, hearing age parameter sets are then copied into the user's hearingprofile.

In some embodiments, the parameters may be calculated directly. Forinstance, the parameters may be calculated by fitting a user maskingcontour curve to a target masking contour curve. Alternately, theparameters may be calculated through the optimization of perceptuallyrelevant information (PRI). Alternately, the parameters may becalculated using commonly known prescriptive techniques in the art.

In some embodiments, the consumer electronic device is one of a mobilephone, a tablet, a television, a desktop computer, a laptop, a hearable,a smart speaker, a headphone and a speaker system.

Unless otherwise defined, all technical terms used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich this technology belongs.

The term “sound personalization algorithm”, as used herein, is definedas any digital signal processing (DSP) algorithm that processes an audiosignal to enhance the clarity of the signal to a listener. The DSPalgorithm may be, for example: an equalizer, an audio processingfunction that works on the subband level of an audio signal, a multibandcompressive system, or a non-linear audio processing algorithm.

The term “audio output device”, as used herein, is defined as any devicethat outputs audio, including, but not limited to: mobile phones,computers, televisions, hearing aids, headphones, smart speakers,hearables, and/or speaker systems.

The term “headphone”, as used herein, is any earpiece bearing atransducer that outputs soundwaves into the ear. The earphone may be awireless hearable, a corded or wireless headphone, a hearable device, orany pair of earbuds.

The term “hearing test”, as used herein, is any test that evaluates auser's hearing health, more specifically a hearing test administeredusing any transducer that outputs a sound wave. The test may be athreshold test or a suprathreshold test, including, but not limited to,a psychophysical tuning curve (PTC) test, a masked threshold (MT) test,a temporal fine structure test (TFS), temporal masking curve test and aspeech in noise test.

The term “server”, as used herein, generally refers to a computerprogram or device that provides functionalities for other programs ordevices.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof, which areillustrated in the appended drawings. Understand that these drawingsdepict only exemplary embodiments of the disclosure and are nottherefore to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 illustrates a flow chart of a server receiving hearing test datainput from a plurality of devices and outputting sound personalizationDSP parameters to a plurality of audio output devices;

FIGS. 2A and 2B illustrate example graphs showing the deterioration ofhuman audiograms and masking thresholds with age, respectively;

FIGS. 3A and 3B illustrate example graphs showing one example of the PTCand MT test paradigms;

FIG. 4 illustrates a method for server-side management of personalizedaudio replay on a plurality of consumer devices according to one or moreaspects of the present disclosure;

FIG. 5 illustrates a method for server-side management of personalizedaudio replay on a plurality of consumer devices according to one or moreaspects of the present disclosure;

FIG. 6 illustrates a method for server-side management of personalizedaudio replay on a plurality of consumer devices according to one or moreaspects of the present disclosure;

FIG. 7 illustrates a method for server-side management of personalizedaudio replay in which a user's parameter settings are updated based ontaking multiple hearing tests over a period of time;

FIG. 8 illustrates a method in which a threshold and suprathreshold testare used to calculate sound personalization parameters;

FIG. 9 conceptually illustrates masked threshold curve widths for threedifferent users, which can be used for best fit and/or nearest fitcalculations;

FIG. 10 conceptually illustrates audiogram plots for three differentusers x, y and z, data points which can be used for best fit and/ornearest fit calculations;

FIG. 11 illustrates a method for parameter calculation using a best-fitapproach;

FIG. 12 illustrates a method for parameter calculation using aninterpolation of nearest-fitting hearing data;

FIG. 13 illustrates a method of attaining ratio and threshold parametersfrom a user masking contour curve;

FIG. 14 illustrates a graph for attaining ratio and threshold parametersfrom a user PTC curve;

FIG. 15 illustrates a method for attaining DSP parameters from userhearing data through the optimization of perceptually relevantinformation.

FIG. 16 illustrates an example system embodiment in which one or moreaspects of the present disclosure can be employed.

DETAILED DESCRIPTION

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting the scope of the embodiments describedherein. Numerous specific details are described to provide a thoroughunderstanding of the disclosure. However, in certain instances,well-known or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure can be references to the same embodiment or anyembodiment; and, such references mean at least one of the embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Various example embodiments of the disclosure are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without departing from the spirit and scopeof the present disclosure.

The systems and methods according to aspects of the present disclosureaddress the problems of accurately and effectively personalizing theaudio output of a plurality of audio output devices, and particularlythe problem of doing so in a consistent manner. By enablingserver-mediated sound personalization based on user hearing testresults, aspects of the present disclosure provide for seamless soundpersonalization between various audio output devices. To this extent,increased user adoption of sound personalization and/or augmentationwill not only enable a richer and crisper listening experience for theuser, but also lead to healthier user behavior while listening to audio.

As illustrated in FIG. 1 , a user may take a hearing test, for instance,on a mobile phone 101, laptop computer 102 or in front of a television109, the results of which may then be outputted to a server 103. Theresulting DSP parameters may then be calculated and stored on the server103. This DSP parameter calculation may be done on the initial device(e.g., 101, 102, 103) on which the hearing test is conducted, on theserver 103, or some combination of the above. The resulting DSPparameters may then be outputted to a plurality of end user devices,which include, but are not limited to, a media player 104, mobile phone105, smart speaker 106, laptop computer 107, and a television set 108,etc. The calculated DSP parameters are subsequently used to provideseamless and consistent sound personalization across each of the variousend user devices 104-108. In some embodiments, a user's hearing testdata is outputted to one or more of the end user devices 104-108, suchthat the DSP parameter calculation may be performed on the end userdevice. In some embodiments, one or more of the end user devices 104-108can be employed to perform the user hearing test, i.e. the initialdevice 101-103 and the end user device 104-108 can be provided as asingle device.

FIGS. 2A-B underscore the importance of sound personalization,illustrating the deterioration of a listener's hearing ability overtime. Starting at the age of 20 years old, humans begin to lose theirability to hear higher frequencies—FIG. 2A (albeit above the spectrum ofhuman speech). This loss steadily becomes worse with age, as noticeabledeclines within the speech frequency spectrum are apparent around theage of 50 or 60. However, these pure tone audiometry findings mask amore complex problem as the human ability to understand speech maydecline much earlier. Although hearing loss typically begins at higherfrequencies, listeners who are aware that they have hearing loss do nottypically complain about the absence of high frequency sounds. Instead,they report difficulties listening in a noisy environment and in hearingout the details in a complex mixture of sounds, such as in a telephonecall. In essence, off-frequency sounds more readily mask a frequency ofinterest for hearing impaired individuals—conversation that was onceclear and rich in detail becomes muddled. As hearing deteriorates, thesignal-conditioning capabilities of the ear begin to break down, andthus hearing impaired listeners need to expend more mental effort tomake sense of sounds of interest in complex acoustic scenes (or miss theinformation entirely). A raised threshold in an audiogram is not merelya reduction in aural sensitivity, but a result of the malfunction ofsome deeper processes within the auditory system that have implicationsbeyond the detection of faint sounds.

To this extent, FIG. 2B illustrates key, discernable age trends insuprathreshold hearing tests. The psychophysical tuning curve (PTC) testis a suprathreshold test that measures an individual's ability todiscern a probe tone (or pulsed signal tone) against a sweeping maskernoise of variable frequency and amplitude. For example, thepsychophysical tuning curve test may be measured for signal tonesbetween frequencies of 500 Hz and 4 kHz, and at a probe level of between20 dB SL and 40 dB SL, in the presence of a masking signal for thesignal tone that sweeps from 50% of the signal tone frequency to 150% ofthe signal tone frequency. Additionally, while the sound level of theprobe tone may be between 20 dB SL and 40 dB SL (although various otherranges, such as 5 dB SL-40 dB SL, are also possible), it is noted thatthe sound level of the sweeping masking signal can exceed, and evensignificantly exceed, the sound level range associated with the probetone. Through the collection of large datasets, key age trends as seenon the rightmost vertical axis in FIG. 2B can be ascertained, allowingfor the accurate parameterization of personalization DSP algorithms. Ina multiband compressive system, for example, the threshold and ratiovalues of each subband signal dynamic range compressor (DRC) can bemodified to reduce problematic areas of frequency masking, whilepost-compression subband signal gain can be further applied in therelevant areas. In the context of FIGS. 2A-B, masked threshold curvesrepresent a similar paradigm for measuring masked threshold. A narrowband of noise, in this instance around 4 kHz, is fixed while a probetone sweeps from 50% of the noise band center frequency to 150% of thenoise band center frequency. Again, key age trends can be ascertainedfrom the collection of large MT datasets.

FIG. 3A-B illustrate graphs showing an example method in which a PTCtest 301 or MT test 305 may be conducted. A psychophysical tuning curve(PTC), consisting of a frequency selectivity contour 304, extracted viabehavioral testing, provides useful data to determine an individual'smasking contours. In one embodiment of the test, a masking band of noise302 is gradually swept across frequency, from below the probe frequency303 to above the probe frequency 303. The user then responds when theycan hear the probe and stops responding when they no longer hear theprobe. This gives a jagged trace that can then be interpolated toestimate the underlying characteristics of the auditory filter through amasking contour curve plot. Other methodologies known in the prior artmay be employed to attain user masking contour curves. For instance, aninverse paradigm may be used in which a probe tone 306 is swept acrossfrequency while a masking band of noise 307 is fixed at a centerfrequency (known as a “masked threshold test” or “MT test”).

FIG. 4 illustrates an exemplary embodiment of the present disclosure inwhich personalized audio replay is carried out on a plurality of audiooutput devices. First, a hearing test is conducted 407 on one of aplurality of audio output devices. The hearing test may be provided byany one of a plurality of hearing test options, including but notlimited to: a masked threshold test (MT test) 401, a pure tone thresholdtest (PTT test) 402, a psychophysical tuning curve test (PTC test) 403,a temporal fine structure test (TFS test) 404, a speech in noise test405, or other suprathreshold test(s) 406.

Next, hearing test results are outputted 408 to a server along with oneor more of a timestamp and a unique user identifier. DSP parameters fora sound personalization algorithm are then calculated and stored 409 inthe server database. The calculated DSP parameters for a given soundpersonalization algorithm may include, but are not limited to: ratio,threshold and gain values within a multiband dynamic processor, gain andlimiter values for equalization DSPs, and/or parameter values common toother sound personalization DSPs (see, e.g., commonly owned U.S. Pat.No. 10,199,047 and U.S. patent application Ser. No. 16/244,727, thecontents of which are incorporated by reference in their entirety).

One or more of the DSP parameter calculations may be performed directlyor indirectly, as is explained below. When a user inputs 410 theirunique identifier on one of a plurality of audio output devices, theuser's DSP parameters are retrieved 411 from the server database andoutputted 412 to the audio output device's sound personalization DSP.The user's unique identifier may be entered into a standaloneapplication on the user's device—or alternatively or additionally, maybe entered into an existing application with a plugin sign-infunctionality that mediates server connectivity. After the user's uniqueidentifier has been received and the corresponding DSP parameter'sretrieved from the server database, audio signals are then locallyprocessed 413 at the given audio output device using the parameterizedsound personalization DSP.

FIG. 5 illustrates an alternative embodiment to the method illustratedin FIG. 4 . As depicted in FIG. 5 , a hearing test (e.g. selected fromthe group of hearing tests 501-506, which in some embodiments can be thesame as the group of hearing tests 401-406) is first conducted 507 onone of a plurality of audio output devices. After conducting the hearingtest, the audio output device then calculates 508 the DSP parametersitself and outputs 509 these locally calculated DSP parameter values andthe user's hearing data to the server database. This output is thenstored 509 at the server database alongside the user's uniqueidentifier.

In contrast to the embodiment of FIG. 4 , where the server received theuser's hearing test data and then performed a server-side calculation ofthe DSP parameters, the embodiment of FIG. 5 performs local calculationof the DSP parameters (i.e. on the same audio output device where thehearing test was performed/measured). However, because the audio outputdevice transmits both the locally calculated DSP parameters and theunderlying hearing test data to the server, it is still possible for aserver-side calculation of the DSP parameters to be performed. Forexample, such a server-side calculation might be used to verify orotherwise augment the local calculation, or as a backup measure.

Subsequently, in a similar fashion to the embodiment of FIG. 4 , when auser inputs 510 their unique identifier in an application on one of aplurality of audio output devices, their DSP parameters are retrieved511 from the server database and outputted 512 to the audio outputdevice's sound personalization DSP. Audio signals are then processed 513accordingly.

FIG. 6 illustrates a further alternative embodiment to the methodsillustrated in FIGS. 4 and 5 . Here, a hearing test is conducted on oneof a plurality of audio output devices 607 and subsequently, the hearingtest data is stored 608 on a server database with a unique useridentifier. In the embodiment of FIG. 6 , DSP parameters for the hearingtest data are not necessarily calculated in response to the hearing testdata being received at the server or server database. Notably, DSPparameters can instead be calculated 611 in an on-demand or just-in-timefashion, either on the end user device, the server, or some combinationof the two, wherein the calculation 611 is based on hearing data that isretrieved 610 from the server database when a user inputs 609 theirunique identifier on the end user device. The calculated DSP parametersmay optionally be outputted to the server for storage alongside theuser's hearing data.

Importantly, the previously discussed methods illustrated in FIGS. 4-6commonly feature a central server which mediates the exchange of datanecessary for sound personalization across a plurality of audio outputdevices, independent of when and where the calculation of the DSPparameter values is performed.

FIG. 7 illustrates a further example embodiment demonstrating the mannerin which a user's calculated DSP parameters may change over time, e.g.,in this case when a user takes multiple hearing tests. As depicted inFIG. 7 , at t1 a user takes an MT test with a 4 kHz noise probe 701 andDSP parameters are then calculated and stored based on the hearing datafrom the MT test, for example, according to any of the methodsillustrated in FIGS. 4-6 . Next, at t2, the user takes an MT test with a1 kHz noise probe 702, representing hearing data within another auditoryfilter region of the individual. Taken together with data collected att1, the combination of both data sets may provide a more comprehensivepicture of the user's hearing health, thus enabling a more accuratecalculation of DSP parameters. To this extent, DSP parameters areupdated in view of the t1 and t2 data. The user may then take furthertests, such as a pure tone threshold test 703 or an MT test with a 2 kHznoise probe 704 to further refine their hearing profile. Additionally,the user may retake a test 705 at a later time point t5, which mayreveal that an earlier test result was aberrant and/or that the user'shearing ability has degraded since the previous test—both scenariosresulting in a parameter update based on the new results. Altogether,the method illustrated in FIG. 7 provides for an evolving and accuratehearing profile that informs updated calculations for soundpersonalization algorithms.

FIG. 8 further illustrates an example embodiment for personalizing audioreplay according to aspects of the present disclosure. Hearing test data801 is obtained from a user and is utilized to calculate soundpersonalization DSP parameters 803, in this instance, for a multibanddynamics processor with at least parameter values ratio (r), threshold(t) and gain (g) for each subband 1 through x. Here, the hearing testdata 801 is provided as MT and PTT data, although other types of hearingtest data can be utilized without departing from the scope of thepresent disclosure. Within the server, hearing data may be stored asexemplary entry [(u_id), t, MT, PTT] 802, wherein u_id is a unique userID, t is a timestamp, MT is hearing data related to an MT test, and PTTis hearing data related to a PTT test. Additionally, when DSP parametersets are calculated for a given sound personalization algorithm, theymay then be added to the entry as [DSP_(q-param)] 804. When a usersubsequently inputs their u_id to log in to a given application on theiraudio output device 805, [DSP_(q-param)] 806 corresponding to the u_idare then outputted to the audio output device 807. In FIG. 8 , parameterset values 803 encompass at least ratio and threshold values for adynamic range compressor as well as gain values per subband signal from1 to x in a multiband dynamics processor sound personalizationalgorithm.

In some embodiments, DSP parameter sets may be calculated directly froma user's hearing data or calculated indirectly based on preexistingentries or anchor points in the server database. An anchor pointcomprises a typical hearing profile constructed based at least in parton demographic information, such as age and sex, in which DSP parametersets are calculated and stored on the server to serve as referencemarkers. Indirect calculation of DSP parameter sets bypasses directparameter sets calculation by finding the closest matching hearingprofile(s) and importing (or interpolating) those values for the user.

FIG. 9 illustrates three conceptual user masked threshold (MT) curvesfor users x, y, and z. The MT curves are centered at frequencies a-d,each with curve width d, which may be used to as a metric to measure thesimilarity between user hearing data. For instance, a root mean squaredifference calculation may be used to determine if user y's hearing datais more similar to user x's or user z's, e.g. by calculating:(√{square root over ((d5a−d1a)²+(d6b−d2b)² . . . )} <√{square root over((d5a−d9a)²+(d6b−d10b)² . . . )}

FIG. 10 illustrates three conceptual audiograms of users x, y and z,each with pure tone threshold values 1-5. Similar to above, a root meansquare difference measurement may also be used to determine, forexample, if user y's hearing data is more similar to user x's than userz's, e.g., by calculating:(√{square root over ((y1−x1)²+(y2−x2)² . . . )} <√{square root over((y1−z1)²+(y2−z2)² . . . )} )As would be appreciated by one of ordinary skill in the art, othermethods may be used to quantify similarity amongst user hearing profilegraphs, where the other methods can include, but are not limited to,methods such as a Euclidean distance measurements, e.g. ((y1−x1)+(y2−x2). . . >(y1−x1)+(y2−x2)) . . . or other statistical methods known in theart. For indirect DSP parameter set calculation, then, the closestmatching hearing profile(s) between a user and other preexistingdatabase entries or anchor points can then be used.

FIG. 11 illustrates an exemplary embodiment for calculating soundpersonalization parameter sets for a given algorithm based onpreexisting entries and/or anchor points. Here, server database entries1102 are surveyed to find the best fit(s) with user hearing data input1101, represented as MT₂₀₀ and PTT₂₀₀ for (u_id)₂₀₀. This may beperformed by the statistical techniques illustrated in FIGS. 9 and 10 .In the example of FIG. 11 , (u_id)₂₀₀ hearing data best matches MT₃ andPTT₃ data 603. To this extent, (u_id)₃ associated parameter sets,[DSP_(q-param 3)], are then used for the (u_id)₂₀₀ parameter set entry,illustrated here as [(u_id)₂₀₀, t₂₀₀, MT₂₀₀, PTT₂₀₀, DSP_(q-param 3)].

FIG. 12 illustrates an exemplary embodiment for calculating soundpersonalization parameter sets for a given algorithm based onpreexisting entries or anchor points, according to aspects of thepresent disclosure. Here, server database entries 1202 are employed tointerpolate 1204 between two nearest fits 1200 with user hearing datainput 1201 MT₃₀₀ and PT₃₀₀ for (u_id)₃₀₀. In this example, the (u_id)₃₀₀hearing data fits nearest between: MT₅≤MT₂₀₀≥MT₃ and PTT₅≤PTT₂₀₀≥PTT₃703. To this extent, (u_id)₃ and (u_id)₅ parameter sets are interpolatedto generate a new set of parameters for the (u_id)₃₀₀ parameter setentry, represented here as [(u_id)₂₀₀, t₂₀₀, MT₂₀₀, PTT₂₀₀,DSP_(q-param3/5)] 705. In a further embodiment, interpolation may beperformed across multiple data entries to calculate soundpersonalization parameters, e.g/

DSP parameter sets may be interpolated linearly, e.g., a DRC ratio valueof 0.7 for user 5 (u_id)₅ and 0.8 for user 3 (u_id)₃ would beinterpolated as 0.75 for user 200 (u_id)₂₀₀ in the example of FIG. 12 ,assuming user 200's hearing data was halfway in-between that of users 3and 5. In some embodiments, DSP parameter sets may also be interpolatednon-linearly, for instance using a squared function, e.g. a DRC ratiovalue of 0.6 for user 5 and 0.8 for user 3 would be non-linearlyinterpolated as 0.75 for user 200 in the example of FIG. 12 .

FIGS. 13, 14 and 15 illustrate various exemplary methods of directlycalculating parameter sets based on user hearing data according to oneor more aspects of the present disclosure. In one embodiment, this maydone using a hearing aid gain table prescriptive formulas. In anotherembodiment, ratio and threshold values for a compressor, as well asgain, in a given multiband dynamic processor signal subband may becalculated by comparing user threshold and suprathreshold informationfor a listener with that of a normal hearing individual, i.e. referenceaudiograms and PTC/MT curves. For instance, masking contour curve data,such as PTC or MT, may be used to calculate ratio and thresholdparameters for a given frequency subband, while audiogram data may beused to calculate gain within a given frequency subband.

FIGS. 13 and 14 demonstrate one way of configuring the ratio andthreshold parameters for a frequency band in a multi-band compressionsystem (see, e.g., commonly owned applications EP18200368.1 and U.S.Ser. No. 16/201,839, the contents of which are herein incorporated byreference). Briefly, a user's masking contour curve is received 1301, atarget masking curve is determined 1302, and is subsequently comparedwith the user masking contour curve 1301 in order to determine andoutput user-calculated DSP parameter sets 1304.

FIG. 14 combines the visualization of a user masking contour curve 1406for a listener (listener) and a target masking contour curve 1407 of aprobe tone 1450 (with the x-axis 1401 being frequency, and the y-axis1402 being the sound level in dB SPL or HL) with an input/output graphof a compressor showing the input level 1403 versus the output level1404 of a sound signal, in decibels relative to full scale (dB FS). Thebisecting line in the input/output graph represents a 1:1 (unprocessed)output of the input signal with gain 1.

The parameters of the multi-band compression system in a frequency bandare threshold 1411 and gain 1412. These two parameters are determinedfrom the user masking contour curve 1406 for the listener and targetmasking contour curve 1407. The threshold 1411 and ratio 1412 mustsatisfy the condition that the signal-to-noise ratio 1421 (SNR) of theuser masking contour curve 1406 at a given frequency 1409 is greaterthan the SNR 1422 of the target masking contour curve 1407 at the samegiven frequency 1409. Note that the SNR is herein defined as the levelof the signal tone compared to the level of the masker noise. Thebroader the curve will be, the greater the SNR. The given frequency 1409at which the SNRs 1421 and 1422 are calculated may be arbitrarilychosen, for example, to be beyond a minimum distance from the probe tonefrequency 1408.

The sound level 1430 (in dB) of the target masking contour curve 1407 ata given frequency corresponds (see bent arrow 1431 in FIG. 14 ) to aninput sound level 1441 entering the compression system. The objective isthat the sound level 1442 outputted by the compression system will matchthe user masking contour curve 1406, i.e., that this sound level 1442 issubstantially equal to the sound level (in dB) of the user maskingcontour curve 1406 at the given frequency 1409. This condition allowsthe derivation of the threshold 1411 (which has to be below the inputsound level 1441) and the ratio 1412. In other words, input sound level1441 and output sound level 1442 determine a reference point of thecompression curve. As noted above, threshold 1411 must be selected to belower than input sound level 1441—if it is not, there will be no change,as below the threshold of the compressor, the system is linear). Oncethe threshold 1411 is selected, the ratio 1412 can be determined fromthe threshold and the reference point of the compression curve.

In the context of the present invention, a masking contour curve isobtained from a user hearing test. A target masking contour curve 1407is interpolated from at least the user masking contour curve 1406 and areference masking contour curve, representing the curve of a normalhearing individual. The target masking contour curve 1407 is preferredover a reference curve because fitting an audio signal to a referencecurve is not necessarily optimal. Depending on the initial hearingability of the listener, fitting the processing according to a referencecurve may cause an excess of processing to spoil the quality of thesignal. The objective is to process the signal in order to obtain a goodbalance between an objective benefit and a good sound quality.

The given frequency 1409 is then chosen. It may be chosen arbitrarily,e.g., at a certain distance from the tone frequency 1408. Thecorresponding sound levels of the listener and target masking contourcurves are determined at this given frequency 1409. The value of thesesound levels may be determined graphically on the y-axis 1402.

The right panel in FIG. 14 (see the contiguous graph) illustrates a hardknee DRC, with a threshold 1411 and a ratio 1412 as parameters that needto be determined. An input sound signal having a sound level 1430/1441at a given frequency 1409 enters the compression system (see bent arrow1431 indicating correspondence between 1430/1441). The sound signalshould be processed by the DRC in such a way that the outputted soundlevel is the sound level of the user masking contour curve 1406 at thegiven frequency 1409. The threshold 1411 should not exceed the inputsound level 1441, otherwise compression will not occur. Multiple sets ofthreshold and ratio parameters are possible. Preferred sets can beselected depending on a fitting algorithm and/or objective fitting datathat have proven to show the most benefit in terms of sound quality. Forexample, either one of the threshold 1411 and ratio 1412 may be chosento have a default value, and the respective other one of the parameterscan then be determined by imposing the above-described condition.

For calculating gain within a subband signal, the results of anaudiogram may be used. For instance, raised thresholds may becompensated for by a corresponding frequency gain.

In one embodiment of the present disclosure, as shown in FIG. 15 , DSPparameters in a multiband dynamic processor may be calculated byoptimizing perceptually relevant information (e.g. perceptual entropy)through parameterization using user threshold and suprathreshold hearingdata (see commonly owned applications U.S. Ser. No. 16/206,376 andEP18208020.0). Briefly, in order to optimally parameterize a multibanddynamic processor through perceptually relevant information, an audiosample 1501, or body of audio samples, is first processed by aparameterized multiband dynamics processor 1502 and the perceptualentropy of the file is calculated 1503 according to user threshold andsuprathreshold hearing data 1507. After calculation, the multibanddynamic processor is re-parameterized 1511 according to a given set ofparameter heuristics, derived from optimization, and from this—the audiosample(s) is reprocessed 1502 and the PRI calculated 1503. In otherwords, the multiband dynamics processor is configured to process theaudio sample so that it has a higher PRI value for the particularlistener, taking into account the individual listener's threshold andsuprathreshold information 1507. To this end, parameterization of themultiband dynamics processor is adapted to increase the PRI of theprocessed audio sample over the unprocessed audio sample. The parametersof the multiband dynamics processor are determined by an optimizationprocess that uses PRI as its optimization criteria.

PRI can be calculated according to a variety of methods found. One suchmethod, also called perceptual entropy, was developed by James D.Johnston at Bell Labs, generally comprising: transforming a sampledwindow of audio signal into the frequency domain, obtaining maskingthresholds using psychoacoustic rules by performing critical bandanalysis, determining noise-like or tone-like regions of the audiosignal, applying thresholding rules for the signal and then accountingfor absolute hearing thresholds. Following this, the number of bitsrequired to quantize the spectrum without introducing perceptiblequantization error is determined. For instance, Painter & Spaniasdisclose a formulation for perceptual entropy in units of bits/s, whichis closely related to ISO/IEC MPEG-1 psychoacoustic model 2 [Painter &Spanias, Perceptual Coding of Digital Audio, Proc. Of IEEE, Vol. 88, No.4 (2000); see also generally Moving Picture Expert Group standardshttps://mpeg.chiariglione.org/standards; both documents included byreference].

Various optimization methods are possible to maximize the PRI of audiosamples, depending on the type of the applied audio processing functionsuch as the above mentioned multiband dynamics processor. For example, asubband dynamic compressor may be parameterized by compressionthreshold, attack time, gain and compression ratio for each subband, andthese parameters may be determined by the optimization process. In somecases, the effect of the multiband dynamics processor on the audiosignal is nonlinear and an appropriate optimization technique such asgradient descend is required. The number of parameters that need to bedetermined may become large, e.g. if the audio signal is processed inmany subbands and a plurality of parameters needs to be determined foreach subband. In such cases, it may not be practicable to optimize allparameters simultaneously and a sequential approach for parameteroptimization may be applied. Although sequential optimization proceduresdo not necessarily result in the optimum parameters, the obtainedparameter values result in increased PRI over the unprocessed audiosample, thereby improving the listener's listening experience.

Other parameterization processes commonly known in the art may be usedto calculate parameters based off user-generated threshold andsuprathreshold information. For instance, common prescription techniquesfor linear and non-linear DSP may be employed. Well known procedures forlinear hearing aid algorithms include POGO, NAL, and DSL. See, e.g., H.Dillon, Hearing Aids, 2^(nd) Edition, Boomerang Press, 2012.

Fine tuning of any of the above mentioned techniques may be estimatedfrom manual fitting data. For instance, it is common in the art to fit amultiband dynamic processor according to series of tests given to apatient in which parameters are adjusted according to a patient'sresponses, e.g. a series of AB tests/decision tree paradigm in which thepatient is asked which set of parameters subjectively sounds better.This testing ultimately guides the optimal parameterization of the DSP.In the instance of the present invention, manually-fit DSP parametersresults of a given hearing profile can be categorized and inputted intothe server database. Subsequently, a user's parameters may be calculatedbased on the approaches delineated in FIGS. 6 and/or 7 .

FIG. 16 shows an example of computing system 1600, which can be forexample any computing device making up (e.g., mobile device 100, server,etc.) or any component thereof in which the components of the system arein communication with each other using connection 1605. Connection 1605can be a physical connection via a bus, or a direct connection intoprocessor 1610, such as in a chipset architecture. Connection 1605 canalso be a virtual connection, networked connection, or logicalconnection.

In some embodiments computing system 1600 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 1600 includes at least one processing unit (CPU orprocessor) 1610 and connection 1605 that couples various systemcomponents including system memory 1615, such as read only memory (ROM)1620 and random access memory (RAM) 1625 to processor 1610. Computingsystem 1600 can include a cache of high-speed memory 1612 connecteddirectly with, in close proximity to, or integrated as part of processor1610.

Processor 1610 can include any general purpose processor and a hardwareservice or software service, such as services 1632, 1634, and 1636stored in storage device 1630, configured to control processor 1610 aswell as a special-purpose processor where software instructions areincorporated into the actual processor design. Processor 1610 mayessentially be a completely self-contained computing system, containingmultiple cores or processors, a bus, memory controller, cache, etc. Amulti-core processor may be symmetric or asymmetric.

To enable user interaction, computing system 1600 includes an inputdevice 1645, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 1600 can also include output device 1635, which can be one ormore of a number of output mechanisms known to those of skill in theart. In some instances, multimodal systems can enable a user to providemultiple types of input/output to communicate with computing system1600. Computing system 1600 can include communications interface 1640,which can generally govern and manage the user input and system output.There is no restriction on operating on any particular hardwarearrangement and therefore the basic features here may easily besubstituted for improved hardware or firmware arrangements as they aredeveloped.

Storage device 1630 can be a non-volatile memory device and can be ahard disk or other types of computer readable media which can store datathat are accessible by a computer, flash memory cards, solid statememory devices, digital versatile disks, cartridges, random accessmemories (RAMS), read only memory (ROM), and/or some combination ofthese devices.

The storage device 1630 can include software services, servers,services, etc., that when the code that defines such software isexecuted by the processor 1610, it causes the system to perform afunction. In some embodiments, a hardware service that performs aparticular function can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as processor 1610, connection 1605, output device 1635,etc., to carry out the function.

The presented technology offers an efficient and accurate way topersonalize audio replay on a plurality of consumer electronic devicesthrough server-mediated sound personalization. It is to be understoodthat the present disclosure contemplates numerous variations, options,and alternatives. For clarity of explanation, in some instances thepresent technology may be presented as including individual functionalblocks including functional blocks comprising devices, devicecomponents, steps or routines in a method embodied in software, orcombinations of hardware and software.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example. The instructions, media for conveyingsuch instructions, computing resources for executing them, and otherstructures for supporting such computing resources are means forproviding the functions described in these disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The invention claimed is:
 1. A method for processing an audio signal,the method comprising: conducting, using a first instance of an audiopersonalization application running on a first audio output device, auser hearing test to obtain a user hearing data; determining, using thefirst instance of the audio personalization application, a set ofcalculated digital signal processing (DSP) parameters, wherein: thefirst instance of the audio personalization application determines theset of calculated DSP parameters based on the user hearing data obtainedfrom the user hearing test; and the set of calculated DSP parameterscorresponds to a particular sound personalization algorithm of the firstaudio output device; providing, to a second audio output devicedifferent from the first audio output device, user-specific informationincluding the set of calculated DSP parameters and informationindicative of the particular sound personalization algorithm, whereinthe user-specific information is associated with a unique identifier ofthe user as reference; receiving, at a second instance of the audiopersonalization application running on the second audio output device, arequest for personalized audio playback, wherein the request forpersonalized audio playback includes a user input comprising the uniqueidentifier of the user; obtaining, at the second instance of the audiopersonalization application, the user-specific information, based on theunique identifier of the user included in the request for personalizedaudio playback; in response to determining that the particular soundpersonalization algorithm associated with the set of calculated DSPparameters in the user-specific information is the same as a soundpersonalization algorithm of the second audio output device, using theset of calculated DSP parameters to parameterize the soundpersonalization algorithm of the second audio output device; andoutputting, by the second audio output device, an audio signal processedby using the sound personalization algorithm of the second audio outputdevice parameterized by the set of calculated DSP parameters receivedfrom the first audio output device.
 2. The method of claim 1, whereinthe first audio output device is a different type of audio output devicefrom the second audio output device.
 3. The method of claim 1, whereinthe first audio output device and the second audio output device are ofthe same type of audio output device.
 4. The method of claim 1, whereindetermining the set of calculated DSP parameters is performed remotefrom one or more of the first audio output device and the second audiooutput device.
 5. The method of claim 4, wherein the set of calculatedDSP parameters are determined on a server and the server-determinedcalculated DSP parameters are stored in a remote database using theunique identifier of the user as reference.
 6. The method of claim 1,wherein determining the set of calculated DSP parameters is furtherperformed based on a user demographic information received as input tothe first instance of the audio personalization application on the firstaudio output device.
 7. The method of claim 1, further comprising:transmitting, by the first instance of the audio personalizationapplication, the user-specific information and the unique identifier ofthe user to a server; and providing, from the server to the second audiooutput device, the user-specific information.
 8. The method of claim 7,wherein the user-specific information is provided from the server to thesecond audio output device based on the receiving, by the server, arequest from the second audio output device that includes the uniqueidentifier of the user.
 9. The method of claim 1, wherein in response toa user conducting an additional hearing test on any one of plurality ofaudio output devices, including the first or second audio output device,the set of calculated DSP parameters is recalculated.
 10. The method ofclaim 9, wherein: the additional hearing test reflects information fromanother auditory filter; or the additional hearing test is a differenttype of hearing test from a previously conducted hearing test; or theadditional hearing test is a more recent version of a previouslyconducted hearing test.
 11. The method of claim 9, wherein theadditional hearing test replaces the hearing data from a previouslyconducted hearing test with aberrant results.
 12. The method of claim11, wherein a parameter set of the parameterized given soundpersonalization algorithm comprises at least one of a threshold value ofa dynamic range compressor provided in each subband, a ratio value of adynamic range compressor provided in each subband, and a gain valueprovided in each subband.
 13. The method of claim 11, wherein the givensound personalization algorithm is a multiband dynamics processor. 14.The method of claim 1, wherein the hearing test is one or more of athreshold test, a suprathreshold test, a psychophysical tuning curvetest, a masked threshold test, a temporal fine structure test, a speechin noise hearing test and a temporal masking curve test.
 15. The methodof claim 1, wherein the hearing test measures masking threshold curveswithin a range of frequencies from 250 Hz to 12 kHz.
 16. The method ofclaim 1, wherein the given sound personalization algorithm operates onsubband signals of the audio signal.
 17. The method of claim 1, wherein:the set of calculated DSP parameters is determined using a best fit ofthe user hearing data with previously inputted hearing data within theserver's database; or the set of calculated DSP parameters is determinedusing a fitted mathematical function derived from plotted hearing andDSP parameter data; and the parameters associated with the best fittingand the previously inputted hearing data are selected to correspond tothe user's parameters.
 18. The method of claim 17, where best fit isdetermined by one of average Euclidean distance and root mean squaredifference.
 19. The method of claim 1, wherein the audio output deviceis one of a mobile phone, a tablet, a television, a laptop computer, ahearable device, a smart speaker, a headphone and a speaker system. 20.An audio processing system comprising: at least one processor; and atleast one memory storing instructions, which when executed cause the atleast one processor to perform actions comprising: conducting, using afirst instance of an audio personalization application running on afirst audio output device, a user hearing test to obtain a user hearingdata; determining, using the first instance of the audio personalizationapplication, a set of calculated digital signal processing (DSP)parameters, wherein: the first instance of the audio personalizationapplication determines the set of calculated DSP parameters based on theuser hearing data obtained from the user hearing test; and the set ofcalculated DSP parameters corresponds to a particular soundpersonalization algorithm of the first audio output device; providing,to a second audio output device different from the first audio outputdevice, user-specific information including the set of calculated DSPparameters and information indicative of the particular soundpersonalization algorithm, wherein the user-specific information isassociated with a unique identifier of the user as reference; receiving,at a second instance of the audio personalization application running onthe second audio output device, a request for personalized audioplayback, wherein the request for personalized audio playback includes auser input comprising the unique identifier of the user; obtaining, atthe second instance of the audio personalization application, theuser-specific information, based on the unique identifier of the userincluded in the request for personalized audio playback; in response todetermining that the particular sound personalization algorithmassociated with the set of calculated DSP parameters in theuser-specific information is the same as a sound personalizationalgorithm of the second audio output device, using the set of calculatedDSP parameters to parameterize the sound personalization algorithm ofthe second audio output device; and outputting, by the second audiooutput device, an audio signal processed by using the soundpersonalization algorithm of the second audio output deviceparameterized by the set of calculated DSP parameters received from thefirst audio output device.